Task-grounded human skill data for physical AI
Batchdim builds curated datasets for teams training robotics, world models, and embodied AI systems. We focus on real-world human work: skilled trades, fine motor tasks, tool use, and motion-rich activities that matter for physical intelligence.
From electricians and auto mechanics to barbers, potters, and musicians, we help teams access high-signal human activity data built for model development.
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The bottleneck
The next bottleneck in physical AI is not only models. It is data quality.
Progress in physical AI depends on data that reflects how people actually act in the real world: using tools, moving through constrained environments, adapting to materials, and executing tasks with variation, precision, and intent.
Most available footage was never created for training. It often lacks task structure, consistent coverage, and the downstream usability needed for robotics and embodied systems.
Batchdim exists to close that gap.
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What Batchdim does
We build datasets around real tasks, not generic footage.
We work with customers to source and curate human skill data that is relevant to physical-world model training. That includes manual workflows, tool interaction, dexterous motion, procedural tasks, and real human demonstrations across a growing set of professions and environments.
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Why this matters
Better data changes what models can learn.
For physical AI, the difference between raw footage and usable training data is substantial. Models need exposure to real human-object interaction, tool use in context, sequential task execution, natural variation across operators, fine motor control, and movement through real environments.
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Current data coverage
Skilled trades
- Electricians
- Auto mechanics
- Carpenters
- Plumbers
- Painters
Fine motor and creative
- Calligraphers
- Potters
- Makeup artists
- Barbers
- Musicians
Motion-rich activity
- Bikers
Need a workflow not listed here? We can scope targeted datasets around specific tasks, operator types, and environments.
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Why teams work with Batchdim
Task-grounded
We organize datasets around what a model needs to learn, not around broad content categories.
High-signal
We prioritize useful interaction data: tools, materials, motion, dexterity, sequence, and real environments.
Operationally useful
The goal is not just collection. It is delivery of data that is more usable inside real model pipelines.
Flexible by domain
We support datasets across trades, creative work, and human activity domains relevant to embodied intelligence.
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Built for teams training physical intelligence
Batchdim supports teams working on robotics, embodied AI, world models, imitation learning, tool-use understanding, manipulation and dexterity, behavior grounding, and multimodal systems operating in the physical world.
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How it works
Scope the capability
We start with the task, behavior, or environment the model needs to understand.
Define coverage
We map the right operators, workflows, conditions, and task variation.
Curate for usability
We refine the data for relevance, consistency, and downstream training value.
Deliver for iteration
You get data shaped around model development, not just raw collection.
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Where Batchdim fits
Batchdim supports teams working on robotics training data, embodied AI systems, world models, imitation learning, tool-use understanding, manipulation and dexterity, behavior grounding, and multimodal physical reasoning.
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From the Batchdim blog
Perspectives on human skill data, physical AI, and what it takes to build useful training datasets.
Coming soon
Tell us what your model needs to learn
If you are training for a specific workflow, profession, tool class, or behavior type, we can help scope the right dataset.